| Challenge: | Recent studies have shown that knowledge graphs are prone to various social biases, and have proposed multiple methods for debiasing them. |
| Approach: | They propose a framework for identifying biases present in knowledge graph embeddings based on numerical bias metrics. |
| Outcome: | The proposed framework can be extended to further bias definitions and applications. |
Similar Papers
The Lifecycle of “Facts”: A Survey of Social Bias in Knowledge Graphs (2022.aacl-main)
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| Challenge: | Knowledge graphs are used in a variety of downstream tasks and in hybrid AI systems. |
| Approach: | They propose to examine the lifecycle of knowledge graphs with respect to bias influences. |
| Outcome: | The proposed models are based on the lifecycle of knowledge graphs and their embedded versions . they show that the KGs manifest biases and propagate harmful prejudices . |
Debiasing knowledge graph embeddings (2020.emnlp-main)
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| Challenge: | Existing methods to train knowledge graph embeddings to be neutral to sensitive attributes such as gender have been shown to increase training time by a factor of eight or more. |
| Approach: | They propose a method where all embeddings are trained to be neutral to sensitive attributes such as gender by default using an adversarial loss. |
| Outcome: | The proposed method reduces training time by eightfold and improves accuracy. |
Model-Agnostic Bias Measurement in Link Prediction (2023.findings-eacl)
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| Challenge: | Existing work investigating social bias in factual knowledge graphs has focused on knowledge graph embeddings, so more recent classes of models achieving superior results by fine-tuning Transformers have not yet been investigated. |
| Approach: | They propose a model-agnostic approach for bias measurement leveraging fairness metrics to compare bias in knowledge graph embedding-based predictions (KG only) with models that use pre-trained, Transformer-based language models (KG+LM). |
| Outcome: | The proposed model-agnostic approach compares gender bias in occupation predictions with models that use pre-trained, Transformer-based language models (KG+LM). |
Identifying and Mitigating Social Bias Knowledge in Language Models (2025.findings-naacl)
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| Challenge: | Existing methods for debiasing may generate incorrect or nonsensical predictions but leave aside individual commonsense facts, resulting in modified knowledge that elicits unreasonable or undesired predictions. |
| Approach: | They propose a framework that identifies encoding locations of biases within language models and then applies the Fairness-Stamp (FAST) they also propose 'BiaScope' to evaluate the retention of commonsense knowledge and generalization across paraphrased social biase. |
| Outcome: | The proposed framework surpasses state-of-the-art baselines with superior debiasing performance while not compromising the overall model capability for knowledge retention and prediction. |
Mind Your Bias: A Critical Review of Bias Detection Methods for Contextual Language Models (2022.findings-emnlp)
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| Challenge: | Existing methods for detection of biases in contextual language models are inconsistent and inconclusive. |
| Approach: | They propose to use word embedding association test to detect biases in contextual language models to compare them with other methods. |
| Outcome: | The proposed methods are inconsistent and inconclusive for language models with word embeddings. |
KGE-CL: Contrastive Learning of Tensor Decomposition Based Knowledge Graph Embeddings (2022.coling-1)
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| Challenge: | Existing knowledge graph embedding methods ignore semantic similarity between related entities and entity-relation couples in different triples . |
| Approach: | They propose a contrastive learning framework for tensor decomposition based (TDB) KGE that can shorten the semantic distance of related entities and entity-relation couples in different triples and thus improve the performance of KGE. |
| Outcome: | The proposed method achieves 51.2% MRR, 46.8% Hits@1 on three standard KGE datasets, 37.8% MRR and 28.6% Hits @1 on FB15k-237 datasets and 59.1% MRR . |
Towards Understanding the Geometry of Knowledge Graph Embeddings (P18-1)
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| Challenge: | Knowledge Graph (KG) embedding has emerged as a very active area of research over the last few years, resulting in the development of several embeddable methods. |
| Approach: | They propose to use KG embedding methods to represent entities and relations as vectors in a high-dimensional space. |
| Outcome: | The proposed methods represent entities and relations in KGs as vectors in a high-dimensional space. |
Predictive Multiplicity of Knowledge Graph Embeddings in Link Prediction (2024.findings-emnlp)
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| Challenge: | Knowledge graph embeddings (KGE) models are often used to predict missing links for knowledge graphs (KGs) however, multiple KG embedds can give conflicting predictions for unseen queries. |
| Approach: | They define predictive multiplicity in link prediction and introduce evaluation metrics to measure it using commonly used benchmark datasets. |
| Outcome: | The proposed methods significantly mitigat conflicts by 66% to 78% in link prediction. |
Understanding Gender Bias in Knowledge Base Embeddings (2022.acl-long)
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| Challenge: | Knowledge base (KB) embeddings have been shown to contain gender biases . authors develop two new bias measures to quantify them and trace their origins in KB . |
| Approach: | They propose two ways to quantify gender biases in knowledge base (KB) embeddings . they use the influence function to inspect the contribution of each triple in KB to the overall group bias . |
| Outcome: | The proposed measures are compared with real-world census data to examine gender biases. |
A Decade of Knowledge Graphs in Natural Language Processing: A Survey (2022.aacl-main)
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| Challenge: | Knowledge graphs (KGs) are a representation of semantic relations between entities . despite their popularity, there is still no general understanding of what exactly a KG is or for what tasks it is applicable. |
| Approach: | They analyze 507 papers on knowledge graphs in natural language processing (NLP) they provide a taxonomy of tasks and review the maturity of individual research streams . |
| Outcome: | The findings summarize the literature and highlight directions for future work. |